RAG Frameworks, Parsers and Vector Databases

Langflow is a visual builder for developers creating AI agents and retrieval-augmented generation (RAG) applications. You can run it locally or on your own server, with Docker support and desktop apps for Windows and macOS. The open-source software uses the MIT license. A hosted cloud offering provides a separate deployment option.

Onyx is an AI search and chat platform for teams whose information is spread across workplace apps. It indexes company knowledge so employees can ask questions across sources and get answers grounded in relevant documents. Teams can deploy it in their own cloud or on bare metal, including an air-gapped environment.

Qdrant is a self-hosted vector database for developers building semantic search, retrieval-augmented generation (RAG), recommendations and AI agent memory. It stores embeddings alongside JSON metadata, so applications can find similar content while restricting results by attributes such as location, text or numeric ranges. The Rust engine is open source under Apache 2.0 and runs locally in Docker or on your own servers.

LightRAG combines knowledge graphs with vector search to answer questions across a document collection. It's a self-hosted Python framework for developers building document assistants, particularly where answers depend on relationships between facts in different files, such as legal or financial material.

Dify is a source-available platform for teams building AI agents and apps on a visual canvas. Its Community Edition runs on your own server with Docker. Dify also offers a hosted cloud service, while Enterprise deployments can run in a VPC or on a self-hosted server. The Community Edition uses a custom Apache 2.0 derivative license.

RAGFlow is an Apache 2.0 licensed RAG engine for teams building AI agents that need to answer questions from their own documents. It can run on a self-hosted server through Docker on Windows, macOS or Linux. A separate hosted cloud service is available.

Open Notebook is a self-hosted alternative to Google's NotebookLM for researchers, students, and professionals who want AI help with their own research materials. It runs locally or on your server through Docker and is open source under the MIT license. You choose which content the AI can access and which provider processes it.

Docling is an MIT-licensed, open source document parser for developers turning files into structured content for search and AI applications. It runs locally on macOS, Linux, and Windows, including in air-gapped environments. Its PDF processing identifies page layout and reading order, extracts tables, code, and formulas, and classifies images.

Open WebUI gives individuals and teams a self-hosted place to chat with local LLMs and cloud models. It runs on your own computer or server, including through Docker, and can work entirely offline with local models.

MinerU parses documents locally into structured text for AI agents, RAG systems and knowledge bases. It's for people working with scanned PDFs, academic papers and Office files whose tables, formulas or page layouts need more care than plain text extraction.

QMD is a local search engine for people with Markdown notes, meeting transcripts, or documentation they want to search themselves or make available to an AI agent. It accepts exact keywords and natural-language queries, with indexing and model inference running on your own machine. It's open source under MIT.

Bisheng is an open source, self-hosted platform for teams building AI applications around business documents and processes. Its visual workflow editor combines automated tasks with human feedback, including intervention during multi-turn conversations. It's suited to document review, support ticket assistance and report generation that need more control than a single chatbot exchange.

MindsDB Query Engine is a self-hosted server for developers building AI agents that need access to business data and documents. It queries connected sources through one SQL dialect and adds semantic search for unstructured content. You can run it locally or on your own server with Docker. Its core uses the Elastic License 2.0, with separate licenses in some directories.

Nougat is a local AI PDF parser for researchers and developers who need scientific papers as usable text, including their equations and tables. It converts academic PDFs into Markdown-style documents with LaTeX notation, so the output retains structure that plain text extraction can lose.

QAnything is a self-hosted knowledge base for people and teams who want to ask questions about their own documents, including collections that mix Chinese and English. It can answer in either language regardless of the document's language, and runs locally through Docker on Windows, macOS and Linux.

FlagEmbedding is an open-source Python toolkit for developers building semantic search or retrieval-augmented generation (RAG) into their own applications. It runs BGE embedding and reranking models, with tools to fine-tune both and evaluate retrieval results. The library uses the MIT license.

Verba is a self-hosted document chatbot for people who want to ask questions across their files and knowledge bases. The project is archived and no longer maintained. It uses Weaviate to find relevant passages and gives those passages to a language model to generate answers.

RAGapp is a self-hosted app for teams that want an AI assistant using retrieval-augmented generation (RAG) on their own infrastructure. It pairs a browser chat interface with an admin interface for configuring the assistant, taking an approach similar to OpenAI's custom GPTs. It's open source under the Apache 2.0 license.

Flowise is a visual builder for AI agents and chatbots that can run locally or on your own server, including through Docker. It's for developers and teams building LLM applications with connected workflow blocks. The project is archived and no longer maintained.

ColPali is a local AI document retrieval library for developers and researchers building document search or retrieval-augmented generation systems. It searches pages as images, using their text, charts and layout together rather than relying on a separate OCR pipeline. The colpali-engine package is deprecated; its maintainers recommend Sentence Transformers for new projects and production use.

pgai keeps search embeddings in sync with PostgreSQL data for developers building RAG applications and AI agents. It's a Python library with database components and workers you can self-host, including in Docker. The project is archived and no longer maintained or supported. Its code is open source under the PostgreSQL License.

chromem-go is a vector database that runs inside your Go application, so developers can add semantic search or retrieval augmented generation (RAG) without maintaining a separate database server. It stores text alongside embeddings and retrieves related documents for use in LLM answers. Its focus is ordinary application workloads rather than collections containing millions of documents.

Quivr Core is a Python framework for developers adding document-based AI answers to their own applications. It combines file ingestion with retrieval-augmented generation (RAG), so a model can answer questions using material from your documents. It supports local models through Ollama as well as cloud APIs from OpenAI, Anthropic, and Mistral.

Cognita is a self-hosted RAG framework for developers building applications that answer questions using their own documents. The project is archived and no longer maintained. It combines a browser interface for document Q&A with reusable components built on LangChain and LlamaIndex, under the Apache 2.0 open-source license.

OGX, formerly Llama Stack, is a self-hosted AI application server for developers building chat apps, document search or AI agents. It brings model inference, file storage, vector search and agent orchestration into one process. You can run it on a laptop, in a datacenter or in the cloud. It's open source under MIT.

DB-GPT is a self-hosted AI data assistant for teams analyzing business data and developers building data applications. It turns plain-language requests into SQL queries and Python analysis, then produces charts, dashboards, or HTML reports. You can run it on macOS or Linux, with Docker deployment also supported.

BotSharp is a self-hosted framework for .NET developers building AI agents into business applications. Written in C#, it runs on Windows, Linux and macOS and is open source software under Apache 2.0. Its plugin design lets teams choose their model provider, storage and interface while keeping agent coordination in the same framework.

Kaito manages self-hosted LLM inference, fine-tuning, and document retrieval services in a Kubernetes cluster. It's for teams that want to run models on infrastructure they control while reducing the work of sizing GPU resources and managing model deployments. The project is open source under Apache 2.0.

LangChainGo is a Go implementation of LangChain for developers building LLM applications in their own software. It connects Go programs to model backends, including Ollama for local LLM use and cloud services such as OpenAI and Gemini. It's a library, so its audience is developers who want to build an application rather than use a ready-made chat interface.

Unstructured is a local document processing library for developers building LLM applications and document ingestion pipelines. It turns PDFs, Word documents, HTML, emails and images into document elements that applications can use. The Python library is open source under Apache 2.0 and runs on your own hardware, including through Docker images for x86_64 and Apple Silicon.

Morphik Core is a self-hosted multimodal retrieval engine for developers building AI applications around visually rich documents. It searches diagrams, schematics, charts, and datasheets alongside text, so applications can retrieve information that text extraction alone can miss. You can run it on your own server, including through Docker, or use Morphik's hosted service.

Marqo Open Source is a self-hosted search engine for developers building semantic document search, image search, or retrieval for AI applications. It handles embedding generation alongside storage and retrieval, so applications can submit documents without maintaining a separate embedding service. The open-source project is deprecated and no longer receives updates.

USearch is an open-source similarity search library for developers building semantic search, recommendation systems, and other applications that compare vectors. It runs within your application on your own hardware or server. Its compact C++ core supports custom definitions of similarity, including comparisons between combined image and text embeddings or geospatial data.

R2R is a self-hosted AI retrieval system for developers building applications that answer questions using their own documents. It combines search, retrieval-augmented generation (RAG) and a reasoning agent behind a REST API. The project is open source under the MIT license and runs as a Python service or in Docker.

docTR is an open-source Python OCR library for developers building document processing tools and researchers comparing text recognition models. It reads PDFs and images on your own hardware, locating words and recognizing their text. The library uses PyTorch and carries the Apache 2.0 license.

xberg, formerly Kreuzberg, is a local document extraction engine for developers building AI search, document processing, and retrieval-augmented generation applications. It reads PDFs, Office files, scanned images, email, and nested archives, extracting text, tables, images, and metadata through one shared engine. It's open source under MIT.